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Highlights • Spatially varying Statistical Soil Moisture Profile (SSMP) model is developed. • SSMP considers Hydrologic Soil Groups (HSGs) to impart spatial transferability. • SSMP model is able to estimate soil moisture profile at ungauged locations. • Promising to assess vertical soil moisture profile over a large area. Abstract Information on vertical Soil Moisture Content (SMC) profile is important for several hydro-meteorological processes. This study borrows the idea of coupling the memory and forcing from a previous study and develops a spatially-varying Statistical Soil Moisture Profile (SSMP) model to estimate the vertical SMC profile. It uses only surface soil moisture (0–5 cm) values and Hydrological Soil Groups (HSGs) information of the location. The focus of the study is incorporation of the HSG information to ensure the spatial transferability of the proposed model by capturing the spatial variations of soil moisture profile with the change in soil hydraulic properties. The wide range of soil moisture data for model development as well as for spatial validation is obtained from 171 stations from different networks of International Soil Moisture Network (ISMN) at five different depths, i.e., 5, 10, 20, 51 and 102 cm. The HSG information at the locations are extracted from the Web Soil Survey (WSS) database. The potential of spatial transferability of the SSMP model is assessed by applying it to the new stations within the corresponding HSG. Model performances are promising for all four depth pairs (5–10, 10–20, 20–51 and 51–102 cm) of all four HSGs during both model development and spatial validation given the model complexity. Hence, the spatially-varying SSMP model is suitable at the ungauged locations by incorporating the HSG information. The potential application of the proposed model shows the future scope to assimilate the satellite based surface SMC data into the model to develop a vertical soil moisture profile map over a large area. [ABSTRACT FROM AUTHOR]

Summary This study presents an novel Bayesian inversion scheme for high-dimensional undetermined TDR waveform inversion. The methodology quantifies uncertainty in the moisture content distribution, using a Gaussian Markov random field (GMRF) prior as regularization operator. A spatial resolution of 1 cm along a 70-cm long TDR probe is considered for the inferred moisture content. Numerical testing shows that the proposed inversion approach works very well in case of a perfect model and Gaussian measurement errors. Real-world application results are generally satisfying. For a series of TDR measurements made during imbibition and evaporation from a laboratory soil column, the average root-mean-square error (RMSE) between maximum a posteriori (MAP) moisture distribution and reference TDR measurements is 0.04 cm 3 cm −3 . This RMSE value reduces to less than 0.02 cm 3 cm −3 for a field application in a podzol soil. The observed model-data discrepancies are primarily due to model inadequacy, such as our simplified modeling of the bulk soil electrical conductivity profile. Among the important issues that should be addressed in future work are the explicit inference of the soil electrical conductivity profile along with the other sampled variables, the modeling of the temperature-dependence of the coaxial cable properties and the definition of an appropriate statistical model of the residual errors. [ABSTRACT FROM AUTHOR]

Highlights: [•] Vertical and horizontal distributions of soil moisture depended on vegetation types. [•] In order of importance, vegetation type, soil depth and the wetness index had effect on temporal stability of soil moisture. [•] Relationships between the standard deviation of relative differences and the wetness index varied nonlinearly with soil depth. [ABSTRACT FROM AUTHOR]

Abstract: The temporal stability of soil moisture profile across the 610km2 Little Washita River Experimental Watershed (LWREW), located in southwestern Oklahoma, is investigated. Experimental data were acquired by time-domain reflectometry (TDR) probes. TDR data were routinely collected at eight locations during the months of June and July in 1997 and in July 2003, coincident with large-scale hydrological remote sensing experiments. Analyses were performed to determine if a subset of the TDR sites could be used to represent watershed averages (i.e. sensor network averages) of soil water content at various levels in the soil profile, as well as in the total profile. The results show that two of the eight TDR sites were temporally stable. One site consistently underestimated and the other consistently overestimated watershed average soil water content at all levels in the soil profile. Because the offset between these under- and over-estimates and the watershed mean are known, these sites can be used to determine the watershed mean values of soil water content at all levels in the profile, as well as to provide ranges of soil water content within the watershed. Identification of these temporally stable sites within the LWERW will assist in the validation of coarse spatial resolution surface soil moisture products derived from remote sensing experiments, as well as providing data sets for watershed hydrologic modeling of subsurface soil water contents. [Copyright &y& Elsevier]

Estimation of soil water content in the root zone with time in different parts of a watershed is important for both strategic and tactical management of water resources, as well as of agricultural production, water quality, and soil resources. This estimation requires detailed knowledge of rainfall intensities and meteorological variables over space and time, as well as the physical and hydraulic properties of the soil horizons and plant growth information. However, all this detailed spatial information is extremely expensive and time consuming to obtain. New technologies are helping to increase the spatial sampling of rainfall and other meteorological variables, but spatially detailed measurement of soil properties is still not practical. The best we can obtain from the existing soil survey database is the spatial distribution of soil textural class. We investigated the use of a hierarchy of limited soil input data, ranging from soil textural class of soil horizons alone, to measured soil texture and bulk densities of horizons, additional lab or field measurement of −33 kPa soil water content, to additional field measurement of average saturated hydraulic conductivity. These five modeling scenarios, along with meteorological and plant information, were input to the Root Zone Water Quality Model (RZWQM) to estimate 0–60 cm soil water content over a 30-day period in 1997 at the Little Washita River Experimental Watershed in Oklahoma. The estimated water contents were compared with time-domain reflectometry (TDR) profile measurements and gravimetric samplings of soil surface moisture. In addition to the five scenarios using limited input data, a more detailed set of data based on laboratory measured soil water retention curves and field measured saturated conductivity was supplied to the model for all Brooks–Corey function parameters (full description mode). Estimates of root zone soil water content using detailed input were compared to estimates obtained using minimum input data. Adjustments in specific hydraulic parameters were also made in an effort to calibrate the model to the soils in this region. Overall, reasonable agreement was found between TDR-measured and RZWQM-predicted average water contents for 0–60 cm depths. Surprisingly, the smallest errors in the predicted water contents were achieved using either the textural class only or the hydraulic properties determined in situ, with root mean square errors ranging from 0.012 to 0.018 m3 m−3. Hence, the model provided adequate estimates of average profile soil water content based on textural class-name only which was considered the most limited input data condition. [Copyright &y& Elsevier]